import glob
import os
import random
import time
from pathlib import Path

import numpy as np
from loguru import logger
from transformers import BertTokenizer

import mindspore as ms

from mindone.diffusers import schedulers
from mindone.diffusers.models import AutoencoderKL
from mindone.transformers import BertModel
from mindone.transformers.modeling_utils import logger as tf_logger

from .constants import NEGATIVE_PROMPT, SAMPLER_FACTORY
from .diffusion.pipeline_controlnet import StableDiffusionControlNetPipeline
from .modules.controlnet import HunYuanControlNet
from .modules.models import HUNYUAN_DIT_CONFIG, HunYuanDiT
from .modules.posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
from .modules.text_encoder import MT5Embedder
from .utils.tools import convert_state_dict, load_state_dict, set_seeds


class Resolution:
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def __str__(self):
        return f"{self.height}x{self.width}"


class ResolutionGroup:
    def __init__(self):
        self.data = [
            Resolution(1024, 1024),  # 1:1
            Resolution(1280, 1280),  # 1:1
            Resolution(1024, 768),  # 4:3
            Resolution(1152, 864),  # 4:3
            Resolution(1280, 960),  # 4:3
            Resolution(768, 1024),  # 3:4
            Resolution(864, 1152),  # 3:4
            Resolution(960, 1280),  # 3:4
            Resolution(1280, 768),  # 16:9
            Resolution(768, 1280),  # 9:16
        ]
        self.supported_sizes = set([(r.width, r.height) for r in self.data])

    def is_valid(self, width, height):
        return (width, height) in self.supported_sizes


STANDARD_RATIO = np.array(
    [
        1.0,  # 1:1
        4.0 / 3.0,  # 4:3
        3.0 / 4.0,  # 3:4
        16.0 / 9.0,  # 16:9
        9.0 / 16.0,  # 9:16
    ]
)
STANDARD_SHAPE = [
    [(1024, 1024), (1280, 1280)],  # 1:1
    [(1280, 960)],  # 4:3
    [(960, 1280)],  # 3:4
    [(1280, 768)],  # 16:9
    [(768, 1280)],  # 9:16
]
STANDARD_AREA = [np.array([w * h for w, h in shapes]) for shapes in STANDARD_SHAPE]


def get_standard_shape(target_width, target_height):
    """
    Map image size to standard size.
    """
    target_ratio = target_width / target_height
    closest_ratio_idx = np.argmin(np.abs(STANDARD_RATIO - target_ratio))
    closest_area_idx = np.argmin(np.abs(STANDARD_AREA[closest_ratio_idx] - target_width * target_height))
    width, height = STANDARD_SHAPE[closest_ratio_idx][closest_area_idx]
    return width, height


def _to_tuple(val):
    if isinstance(val, (list, tuple)):
        if len(val) == 1:
            val = [val[0], val[0]]
        elif len(val) == 2:
            val = tuple(val)
        else:
            raise ValueError(f"Invalid value: {val}")
    elif isinstance(val, (int, float)):
        val = (val, val)
    else:
        raise ValueError(f"Invalid value: {val}")
    return val


def get_pipeline(args, vae, text_encoder, tokenizer, model, rank, embedder_t5, infer_mode, controlnet, sampler=None):
    """
    Get scheduler and pipeline for sampling. The sampler and pipeline are both
    based on diffusers and make some modifications.

    Returns
    -------
    pipeline: StableDiffusionControlNetPipeline
    sampler_name: str
    """
    sampler = sampler or args.sampler

    # Load sampler from factory
    kwargs = SAMPLER_FACTORY[sampler]["kwargs"]
    scheduler = SAMPLER_FACTORY[sampler]["scheduler"]

    # Update sampler according to the arguments
    kwargs["beta_schedule"] = args.noise_schedule
    kwargs["beta_start"] = args.beta_start
    kwargs["beta_end"] = args.beta_end
    kwargs["prediction_type"] = args.predict_type

    # Build scheduler according to the sampler.
    scheduler_class = getattr(schedulers, scheduler)
    scheduler = scheduler_class(**kwargs)
    logger.debug(f"Using sampler: {sampler} with scheduler: {scheduler}")

    # Set timesteps for inference steps.
    scheduler.set_timesteps(args.infer_steps)

    # Only enable progress bar for rank 0
    progress_bar_config = {} if rank == 0 else {"disable": True}

    pipeline = StableDiffusionControlNetPipeline(
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=model,
        scheduler=scheduler,
        feature_extractor=None,
        safety_checker=None,
        requires_safety_checker=False,
        progress_bar_config=progress_bar_config,
        embedder_t5=embedder_t5,
        infer_mode=infer_mode,
        controlnet=controlnet,
    )

    return pipeline, sampler


class End2End(object):
    def __init__(self, args, models_root_path):
        self.args = args

        # Check arguments
        t2i_root_path = Path(models_root_path) / "t2i"
        self.root = t2i_root_path
        logger.info(f"Got text-to-image model root path: {t2i_root_path}")

        # Disable BertModel logging checkpoint info
        tf_logger.setLevel("ERROR")

        # ========================================================================
        logger.info("Loading CLIP Text Encoder...")
        text_encoder_path = self.root / "clip_text_encoder"
        self.clip_text_encoder = BertModel.from_pretrained(str(text_encoder_path), False, revision=None)
        logger.info("Loading CLIP Text Encoder finished")

        # ========================================================================
        logger.info("Loading CLIP Tokenizer...")
        tokenizer_path = self.root / "tokenizer"
        self.tokenizer = BertTokenizer.from_pretrained(str(tokenizer_path))
        logger.info("Loading CLIP Tokenizer finished")

        # ========================================================================
        logger.info("Loading T5 Text Encoder and T5 Tokenizer...")
        t5_text_encoder_path = self.root / "mt5"
        embedder_t5 = MT5Embedder(t5_text_encoder_path, mindspore_dtype=ms.float16, max_length=256)
        self.embedder_t5 = embedder_t5
        logger.info("Loading t5_text_encoder and t5_tokenizer finished")

        # ========================================================================
        logger.info("Loading VAE...")
        vae_path = self.root / "sdxl-vae-fp16-fix"
        self.vae = AutoencoderKL.from_pretrained(str(vae_path))
        logger.info("Loading VAE finished")

        # ========================================================================
        # Create model structure and load the checkpoint
        logger.info("Building HunYuan-DiT model...")
        model_config = HUNYUAN_DIT_CONFIG[self.args.model]
        self.patch_size = model_config["patch_size"]
        self.head_size = model_config["hidden_size"] // model_config["num_heads"]
        self.resolutions, self.freqs_cis_img = self.standard_shapes()  # Used for TensorRT models
        self.image_size = _to_tuple(self.args.image_size)
        latent_size = (self.image_size[0] // 8, self.image_size[1] // 8)

        self.infer_mode = self.args.infer_mode
        if self.infer_mode in ["fa", "mindspore"]:
            # Build model structure
            self.model = HunYuanDiT(
                self.args,
                input_size=latent_size,
                **model_config,
                log_fn=logger.info,
            ).half()  # Force to use fp16
            self.controlnet = HunYuanControlNet(
                self.args,
                input_size=latent_size,
                **model_config,
                log_fn=logger.info,
            ).half()
            # Load model checkpoint
            self.load_mindspore_weights()

            lora_ckpt = args.lora_ckpt
            if lora_ckpt is not None and lora_ckpt != "":
                logger.info(f"Loading Lora checkpoint {lora_ckpt}...")

                self.model.load_adapter(lora_ckpt)
                self.model.merge_and_unload()

            logger.info("Loading mindspore model finished")
            logger.info("Loading controlnet finished")
        else:
            raise ValueError(f"Unknown infer_mode: {self.infer_mode}")

        # ========================================================================
        # Build inference pipeline. We use a customized StableDiffusionControlNetPipeline.
        logger.info("Loading inference pipeline...")
        self.pipeline, self.sampler = self.load_sampler()
        logger.info("Loading pipeline finished")

        # ========================================================================
        self.default_negative_prompt = NEGATIVE_PROMPT
        logger.info("==================================================")
        logger.info("                Model is ready.                  ")
        logger.info("==================================================")

    def load_mindspore_weights(self):
        load_key = self.args.load_key
        # get base model path
        if self.args.dit_weight is not None:
            dit_weight = self.args.dit_weight
            if os.path.isdir(dit_weight):
                files = glob.glob(os.path.join(dit_weight, "*.safetensors"))
                if len(files) == 0:
                    raise ValueError(f"No model weights found in {dit_weight}")
                if str(files[0]).split("/")[-1].startswith("pytorch_model_"):
                    model_path = os.path.join(dit_weight, f"pytorch_model_{load_key}.safetensors")
                else:
                    raise ValueError(
                        f"Invalid model path: {dit_weight} with unrecognized weight format: "
                        f"{list(map(str, files))}. When given a directory as --dit-weight, only "
                        f"`pytorch_model_*.safetensors`(provided by HunyuanDiT official) and can be parsed. If you want to load a "
                        f"specific weight file, please provide the full path to the file."
                    )
            elif os.path.isfile(dit_weight):
                model_path = dit_weight
            else:
                raise ValueError(f"Invalid model path: {dit_weight}")
        else:
            model_dir = os.path.join(self.root, "model")
            model_path = os.path.join(model_dir, f"pytorch_model_{load_key}.safetensors")

        # get controlnet model path
        if self.args.controlnet_weight is not None:
            controlnet_weight = self.args.controlnet_weight
            if os.path.isdir(controlnet_weight):
                controlnet_dir = controlnet_weight
                controlnet_path = os.path.join(
                    controlnet_dir, f"pytorch_model_{self.args.control_type}_{load_key}.safetensors"
                )
            elif os.path.isfile(controlnet_weight):
                controlnet_path = controlnet_weight
            else:
                raise ValueError(f"Invalid controlnet path: {controlnet_weight}")
        else:
            controlnet_dir = self.root / "controlnet"
            controlnet_path = controlnet_dir / f"pytorch_model_{self.args.control_type}_{load_key}.safetensors"

        if not os.path.exists(model_path):
            raise ValueError(f"model_path not exists: {model_path}")
        if not os.path.exists(controlnet_path):
            raise ValueError(f"controlnet_path not exists: {controlnet_path}")

        logger.info(f"Loading mindspore model {model_path}...")
        state_dict = load_state_dict(model_path)
        state_dict = convert_state_dict(self.model, state_dict)
        local_state = {k: v for k, v in self.model.parameters_and_names()}
        for k, v in state_dict.items():
            if k in local_state:
                v.set_dtype(local_state[k].dtype)
            else:
                pass  # unexpect key keeps origin dtype
        ms.load_param_into_net(self.model, state_dict, strict_load=True)

        logger.info(f"Loading controlnet model {controlnet_path}...")
        controlnet_state_dict = load_state_dict(controlnet_path)
        if "module" in controlnet_state_dict:
            controlnet_state_dict = controlnet_state_dict["module"]
        controlnet_state_dict = convert_state_dict(self.controlnet, controlnet_state_dict)
        controlnet_local_state = {k: v for k, v in self.controlnet.parameters_and_names()}
        for k, v in controlnet_state_dict.items():
            if k in controlnet_local_state:
                v.set_dtype(controlnet_local_state[k].dtype)
            else:
                pass  # unexpect key keeps origin dtype
        ms.load_param_into_net(self.controlnet, controlnet_state_dict, strict_load=True)

    def load_sampler(self, sampler=None):
        pipeline, sampler = get_pipeline(
            self.args,
            self.vae,
            self.clip_text_encoder,
            self.tokenizer,
            self.model,
            rank=0,
            embedder_t5=self.embedder_t5,
            infer_mode=self.infer_mode,
            sampler=sampler,
            controlnet=self.controlnet,
        )
        return pipeline, sampler

    def calc_rope(self, height, width):
        th = height // 8 // self.patch_size
        tw = width // 8 // self.patch_size
        base_size = 512 // 8 // self.patch_size
        start, stop = get_fill_resize_and_crop((th, tw), base_size)
        sub_args = [start, stop, (th, tw)]
        rope = get_2d_rotary_pos_embed(self.head_size, *sub_args)
        return rope

    def standard_shapes(self):
        resolutions = ResolutionGroup()
        freqs_cis_img = {}
        for reso in resolutions.data:
            freqs_cis_img[str(reso)] = self.calc_rope(reso.height, reso.width)
        return resolutions, freqs_cis_img

    def predict(
        self,
        user_prompt,
        image,
        height=1024,
        width=1024,
        seed=None,
        enhanced_prompt=None,
        negative_prompt=None,
        infer_steps=100,
        guidance_scale=6,
        batch_size=1,
        src_size_cond=(1024, 1024),
        sampler=None,
        use_style_cond=False,
    ):
        # ========================================================================
        # Arguments: seed
        # ========================================================================
        if seed is None:
            seed = random.randint(0, 1_000_000)
        if not isinstance(seed, int):
            raise TypeError(f"`seed` must be an integer, but got {type(seed)}")
        generator = set_seeds(seed)
        # ========================================================================
        # Arguments: target_width, target_height
        # ========================================================================
        if width <= 0 or height <= 0:
            raise ValueError(f"`height` and `width` must be positive integers, got height={height}, width={width}")
        logger.info(f"Input (height, width) = ({height}, {width})")
        if self.infer_mode in ["fa", "mindspore"]:
            # We must force height and width to align to 16 and to be an integer.
            target_height = int((height // 16) * 16)
            target_width = int((width // 16) * 16)
            logger.info(f"Align to 16: (height, width) = ({target_height}, {target_width})")
        elif self.infer_mode == "trt":
            target_width, target_height = get_standard_shape(width, height)
            logger.info(f"Align to standard shape: (height, width) = ({target_height}, {target_width})")
        else:
            raise ValueError(f"Unknown infer_mode: {self.infer_mode}")

        # ========================================================================
        # Arguments: prompt, new_prompt, negative_prompt
        # ========================================================================
        if not isinstance(user_prompt, str):
            raise TypeError(f"`user_prompt` must be a string, but got {type(user_prompt)}")
        user_prompt = user_prompt.strip()
        prompt = user_prompt

        if enhanced_prompt is not None:
            if not isinstance(enhanced_prompt, str):
                raise TypeError(f"`enhanced_prompt` must be a string, but got {type(enhanced_prompt)}")
            enhanced_prompt = enhanced_prompt.strip()
            prompt = enhanced_prompt

        # negative prompt
        if negative_prompt is None or negative_prompt == "":
            negative_prompt = self.default_negative_prompt
        if not isinstance(negative_prompt, str):
            raise TypeError(f"`negative_prompt` must be a string, but got {type(negative_prompt)}")

        # ========================================================================
        # Arguments: style. (A fixed argument. Don't Change it.)
        # ========================================================================
        if use_style_cond:
            # Only for hydit <= 1.1
            style = ms.tensor([0, 0] * batch_size)
        else:
            style = None

        # ========================================================================
        # Inner arguments: image_meta_size (Please refer to SDXL.)
        # ========================================================================
        if src_size_cond is None:
            size_cond = None
            image_meta_size = None
        else:
            # Only for hydit <= 1.1
            if isinstance(src_size_cond, int):
                src_size_cond = [src_size_cond, src_size_cond]
            if not isinstance(src_size_cond, (list, tuple)):
                raise TypeError(f"`src_size_cond` must be a list or tuple, but got {type(src_size_cond)}")
            if len(src_size_cond) != 2:
                raise ValueError(f"`src_size_cond` must be a tuple of 2 integers, but got {len(src_size_cond)}")
            size_cond = list(src_size_cond) + [target_width, target_height, 0, 0]
            image_meta_size = ms.tensor([size_cond] * 2 * batch_size)

        # ========================================================================
        start_time = time.time()
        logger.debug(
            f"""
                       prompt: {user_prompt}
              enhanced prompt: {enhanced_prompt}
                         seed: {seed}
              (height, width): {(target_height, target_width)}
              negative_prompt: {negative_prompt}
                   batch_size: {batch_size}
               guidance_scale: {guidance_scale}
                  infer_steps: {infer_steps}
              image_meta_size: {size_cond}
        """
        )
        reso = f"{target_height}x{target_width}"
        if reso in self.freqs_cis_img:
            freqs_cis_img = self.freqs_cis_img[reso]
        else:
            freqs_cis_img = self.calc_rope(target_height, target_width)

        if sampler is not None and sampler != self.sampler:
            self.pipeline, self.sampler = self.load_sampler(sampler)

        samples = self.pipeline(
            height=target_height,
            width=target_width,
            prompt=prompt,
            negative_prompt=negative_prompt,
            num_images_per_prompt=batch_size,
            guidance_scale=guidance_scale,
            num_inference_steps=infer_steps,
            image_meta_size=image_meta_size,
            style=style,
            return_dict=False,
            generator=generator,
            freqs_cis_img=freqs_cis_img,
            use_fp16=self.args.use_fp16,
            learn_sigma=self.args.learn_sigma,
            image=image,
            control_weight=eval(self.args.control_weight),
        )[0]
        gen_time = time.time() - start_time
        logger.debug(f"Success, time: {gen_time}")

        return {
            "images": samples,
            "seed": seed,
        }
